Extracellular matrix metalloproteinase inducer in brain ischemia and intracerebral hemorrhage
Bibliographic record
Abstract
Increasing evidence from preclinical and clinical studies link neuroinflammation to secondary brain injury after stroke, which includes brain ischemia and intracerebral hemorrhage (ICH). Extracellular matrix metalloproteinase inducer (EMMPRIN), a cell surface transmembrane protein, is a key factor in neuroinflammation. It is widely elevated in several cell types after stroke. The increased EMMPRIN appears to regulate the expression of matrix metalloproteinases (MMPs) and exacerbate the pathology of stroke-induced blood-brain barrier dysfunction, microvascular thrombosis and neuroinflammation. In light of the neurological effects of EMMPRIN, we present in this review the complex network of roles that EMMPRIN has in brain ischemia and ICH. We first introduce the structural features and biological roles of EMMPRIN, followed by a description of the increased expression of EMMPRIN in brain ischemia and ICH. Next, we discuss the pathophysiological roles of EMMPRIN in brain ischemia and ICH. In addition, we summarize several important treatments for stroke that target the EMMPRIN signaling pathway. Finally, we suggest that EMMPRIN may have prospects as a biomarker of stroke injury. Overall, this review collates experimental and clinical evidence of the role of EMMPRIN in stroke and provides insights into its pathological mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".